Learning to Prompt: Improving Student Engagement with Adaptive LLM-based High-School Tutoring

arXiv:2606.20138 · cs.AI, cs.CL, cs.HC, cs.LG · Submitted 2026-06-18 · Read on arXiv

cs.AI, cs.CL, cs.HC, cs.LG

Submitted: 2026-06-18

Updated: 2026-08-26

Terminology

• Scaffolding — 23× in this paper · explained in 3 episodes
• Accuracy — 12× in this paper · explained in 1 episode
• Adaptation — 12× in this paper · explained in 1 episode
• Alignment — 11× in this paper · explained in 1 episode
• Embeddings — 10× in this paper · explained in 2 episodes
• Benchmark — 8× in this paper · explained in 2 episodes
• Sparsity — 8× in this paper · explained in 4 episodes
• Actor — 7× in this paper · explained in 3 episodes
• Calibration — 6× in this paper · explained in 7 episodes
• Reinforcement Learning — 5× in this paper · explained in 4 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Core — 4× in this paper · explained in 2 episodes
• Adam — 3× in this paper · explained in 2 episodes
• Latent Space — 3× in this paper · explained in 7 episodes
• Online Adaptation — 3× in this paper · explained in 3 episodes
• Regret — 3× in this paper · explained in 3 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Sim-to-Real Gap — 3× in this paper · explained in 6 episodes
• Abstraction — 2× in this paper · explained in 1 episode
• Action Space — 2× in this paper · explained in 1 episode
• Anisotropy — 2× in this paper · explained in 3 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Cognitive Load — 2× in this paper · explained in 2 episodes
• Personalization — 2× in this paper · explained in 2 episodes
• Sample Efficiency — 2× in this paper · explained in 4 episodes
• Activation Function — 1× in this paper · explained in 1 episode
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Adaptability — 1× in this paper · explained in 1 episode
• Agentic Task — 1× in this paper · explained in 1 episode
• Agentic Tasks — 1× in this paper · explained in 1 episode
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Frustration — 1× in this paper · explained in 2 episodes
• I Dsubj — 1× in this paper
• LLM-as-a-Judge — 1× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Mode Collapse — 1× in this paper · explained in 2 episodes
• Prompt Engineering — 1× in this paper · explained in 4 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes
• Score Function — 1× in this paper · explained in 4 episodes
• Sim-to-Real Transfer — 1× in this paper · explained in 2 episodes
• Sycophancy — 1× in this paper · explained in 4 episodes

Sources

• Concrete Problems in AI Safety
• Constitutional AI: Harmlessness from AI Feedback
• Representation Degeneration Problem in Training Natural Language Generation Models
• Decoupled Weight Decay Regularization
• OpenAI GPT-5 System Card
• Proximal Policy Optimization Algorithms
• Text Embeddings by Weakly-Supervised Contrastive Pre-training
• Large Language Models Are Human-Level Prompt Engineers

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